KR102129324B1 - Product distribution method and server using auction - Google Patents

Abstract

The embodiments include receiving auction information for a product provided by the seller, including the seller information, the product quantity, the maximum estimated price for the product and the starting price suggested by the seller, providing the auction information to the user, Receiving bidding information including a bidding price between the maximum estimated price and the starting price and a bidding quantity less than or equal to the product quantity from one or more bidders, if a preset time has elapsed, the winning bidder group is selected based on the product quantity and the bidding information Determining the step, determining the selling price for the product as the highest bidder among the bidders of the non-winner who is not included in the successful bidder group, and applying the selling price to the successful bidder group to request payment It relates to a distribution method and a server.

Description

Product distribution method and server using auction {PRODUCT DISTRIBUTION METHOD AND SERVER USING AUCTION}

The present invention relates to product distribution using an auction, and more specifically, a product with a limited quantity is distributed using an auction, but in the auction, the successful bidder has an amount lower than the suggested bid price (for example, the minimum desired by the bidder and the seller) It relates to a technology that provides goods to all sellers, buyers, and distribution service providers by receiving goods at a guaranteed price, which is the guaranteed price of, and calculating a fee based on the amount obtained by subtracting the starting price from the selling price.

Recently, due to advances in science and technology such as shooting technology and printing technology, digital prints, a new type of work that can be purchased at a lower price point while being able to enjoy the original feeling by creating the same replica as the original, are in the spotlight. Digital engraving is a kind of reproduction that only produces the original picture with a certain edition number with the permission of the author. For example, similar to the photo printing method, the original picture is photographed in high resolution and then printed with pigment, a kind of pigment, and put in a compressed acrylic picture frame. It is produced in a limited production method or in a variety of other production methods.

Since digital prints are generally distributed at a much lower price than the original work, it is easier for the general public to own the work of a famous artist in the distribution of works of famous artists, and the selling price is lower than the price of the original in distribution of works of young artists. As it can be introduced to the market, there is a higher possibility that sales are likely to be successful.

However, digital prints are also sold through the traditional work market. Conventional works market is composed of sellers who produce and sell works, buyers who buy works, and intermediaries (eg, galleries, galleries, auction companies, etc.) that intervene in such works. Order has a structure led by an intermediary. Therefore, in general, writers or young artists who have relatively little awareness had the disadvantage of selling their works at an unfavorable entry fee, and there was no inconvenience in having to spend a considerable amount of time (about 75% of the total contract time) even if they negotiated to make a favorable fee. there was. In addition, most intermediaries sometimes receive a purchase fee for a buyer (for example, an auction successful bidder) while receiving an entry fee from a seller.

That is, it is a reality that the technology for distributing a limited quantity of products such as Korean won or digital print is insufficient to benefit all of the seller, buyer, and broker.

Patent Registration Publication No. 10-1115802

The present invention distributes a product with a limited quantity by using an auction, but in the auction, the successful bidder sells the product at a predetermined price lower than the suggested bid price, for example, a selling price between the bid price and the minimum guaranteed price desired by the seller. It is an object of the present invention to provide a product distribution server and method using an auction that provides profits to both sellers, buyers, and distribution service providers by calculating fees based on the amount received and subtracting the starting price from the selling price.

A product distribution method using an auction performed by a product distribution server according to an aspect of the present invention includes receiving auction information about a product provided by a seller; Providing the auction information to a user; Receiving, from one or more bidders, bidding information including a bidding price between a maximum estimated price and the starting price and a bidding quantity less than or equal to the product quantity; Determining a successful bidder group based on the product quantity and the bidding information when a preset time has elapsed; Determining a selling price for the product as the highest bid among non-winners who are not included in the successful bidder group; And requesting payment by applying the selling price to the successful bidder group. Here, the auction information includes seller information, product quantity, a maximum estimated price for the product, and a starting price suggested by the seller.

In one embodiment, determining the selling price comprises: determining one or more subgroups from the bidding price in the winning bidder group; The method may include determining a highest bid among the bidders of the lower bidder in each subgroup as the selling price for each subgroup.

In one embodiment, the maximum estimated price may be determined based on information related to another auction having the same starting price as the starting price.

In one embodiment, the product distribution method may further include calculating a fee for the seller based on an amount obtained by subtracting the starting price from the selling price.

In one embodiment, the product distribution method may further include differentially granting a certain benefit based on the bid price of the successful bidder included in the successful bidder group.

In one embodiment, in the product distribution method, when a part of the successful bidders included in the successful bidder group does not perform payment in response to a payment request, updating the successful bidder group to add a successful bidder based on the bid price of the non- successful bidder It may further include.

In one embodiment, the product distribution method may further include determining the bid price of the additional successful bidder as the sale price for the additional successful bidder.

In one embodiment, the product distribution method calculates the likelihood of bidding during the auction with the product quantity based on the product quantity and the starting price included in the received auction information, before receiving the auction information and providing the auction information to the user. step; And when the likelihood of the bidding is less than a predetermined probability, sending a bidding alert to the seller.

In one embodiment, the product distribution method further includes the step of transmitting the product quantity corresponding to the predetermined possibility to the seller as a product quantity recommendation when the likelihood of probability is less than a predetermined probability based on a result of calculating the probability of the bidding. can do.

In one embodiment, the probability of the patrol is at least one of support vector machine (SVM), support vector data description (SVDD), support vector regression (SVR), artificial neural network (ANN), and K-means clustering algorithm. It can be calculated by the patrol likelihood model generated using one.

In one embodiment, the product distribution method may further include, when the product is the original, sending a request to the non-winner of the auction to confirm whether to purchase a replica of the product.

A product distribution server using an auction according to another aspect of the present invention receives auction information about a product, provides the auction information to a user, and includes a bidding price including a bidding price between the maximum estimated price and the starting price from one or more bidders An auction unit that receives information; A successful bidder determination unit determining a successful bidder group based on the product quantity information and the bidding information when a preset time has elapsed; A selling price determining unit determining a selling price for the product as the highest bidding price among non-winning bidders who are not included in the successful bidder group; And a payment unit for requesting payment by applying the selling price to the successful bidder group. Here, the auction information includes seller information, product quantity information, maximum estimated price information for the product, and starting price information provided by the seller.

According to the product distribution server using an auction according to an aspect of the present invention, the seller, the buyer and the distribution service provider provides benefits.

On the seller's side, the seller can sell the product at a higher starting price, which is the minimum guaranteed amount desired, so that the minimum guaranteed amount is always guaranteed when closing a transaction.

On the distribution service provider's side, the distribution service provider calculates a fee based on the sum of the selling price minus the starting price, so that the seller can create a profit without damage and easily attract other sellers.

In addition, according to the present invention, it is possible to first produce a product after securing a successful bidder through auction, thereby minimizing unsold work. Due to this, it is possible to minimize the burden of separately storing the unsold work.

On the buyer's side, at the auction, all successful bidders included in the successful bidder can purchase the product at the lowest bid price offered by the highest bidder among the successful bidders who are not included in the successful bidder group as the selling price between the bidder and the starting price. Can be provided.

In addition, since the fee is settled based on the selling price and the starting price, it is possible to prevent the irrationality that the successful bidder is charged with the successful bidder for an unexpected amount of money added to the auction.

Due to this, the fixed purchase fee arbitrarily set by the intermediary is settled, and in the case of the successful bidder, the unreasonable problem of the existing art auction, in which an unreasonable fee is included in the payment amount of the successful bidder without notice.

It can also help ensure that the upper limit of the auction budget is calculated. In the current offline auction, the purchase fee is announced or announced through the terms attached to the auction company's training or catalog before the auction starts, and in the online auction, the purchase fee is announced through the terms of the website. (Of course, the fee may vary depending on the contract.) The buyer must bid for the auction by considering this, but if canceled after the successful bid, generally 30% of the successful bid amount is additionally paid as a penalty. However, at the time of bidding, the bidding price is not included in the bidding price. According to the present invention, an additional amount, such as a penalty fee, may not be charged to the auction participant, so that the auction participant can establish a more efficient purchasing strategy.

Furthermore, it is possible to overcome the problem of the original, which is too expensive, and to help new artists sell their products, thereby improving the unbalanced market situation.

The effects of the present invention are not limited to the effects mentioned above, and other effects not mentioned will be clearly understood by those skilled in the art from the description of the claims.

1 is a block diagram of a product distribution server using an auction according to an embodiment of the present invention.
2 is a diagram schematically showing the operation of the product distribution server according to an embodiment of the present invention.
3 is a conceptual diagram illustrating a structure in which auction history information is analyzed according to an embodiment of the present invention.
4 is a flowchart of a product distribution method using an auction according to an embodiment of the present invention.
5 is a diagram illustrating a process in which a selling price is determined according to an embodiment of the present invention.

The terminology used herein is only for referring to a specific embodiment, and is not intended to limit the present invention. The singular forms used herein also include plural forms unless the phrases clearly indicate the opposite. As used herein, the meaning of “comprising” embodies a particular property, region, integer, step, action, element, and/or component, and the presence or presence of another property, region, integer, step, action, element, and/or component. It does not exclude addition.

Although not defined differently, all terms including technical terms and scientific terms used herein have the same meaning as those generally understood by those skilled in the art to which the present invention pertains. Commonly used dictionary-defined terms are additionally interpreted as having meanings consistent with related technical documents and currently disclosed contents, and are not interpreted as ideal or very formal meanings unless defined.

Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings.

1 is a block diagram of a product distribution server using an auction according to an embodiment of the present invention, and FIG. 2 is a diagram schematically showing the operation of a product distribution server according to an embodiment of the present invention.

Referring to FIG. 1, the product distribution server 1 includes an auction generating unit 110, an auction unit 10 including a bid proceeding unit 130, a successful bidder determining unit 40, a selling price determining unit 50, and a payment unit 60 and a control unit 90. In one embodiment, the product distribution server 1 may further include a storage unit 70 and an analysis unit 80.

Referring to FIG. 2, in the auction proceeding through the product distribution server 1, all successful bidders included in the successful bidder group may be provided with the product at a lower price than the suggested bid price.

Referring again to FIG. 1, the product distribution server 1 according to the embodiments may have aspects that are entirely hardware, entirely software, or partially hardware and partially software. For example, the product distribution server may collectively refer to hardware equipped with data processing capability and operating software for driving the same. The terms "unit", "system" and "device" in this specification are intended to refer to a combination of hardware and software driven by the hardware. For example, the hardware may be a data processing device including a central processing unit (CPU), a graphics processing unit (GPU), or other processor. Also, the software may refer to a running process, an object, an executable, a thread of execution, a program, or the like.

Each part (10, 30, 40, 50, 60, 70, 80, 90) constituting the product distribution server 1 according to the embodiments is not intended to refer to a separate component that is necessarily physically separated Does not. That is, in FIG. 1, although shown as separate blocks, the auction unit 10, the successful bidder determination unit 40, the selling price determination unit 50, the payment unit 60, the storage unit (depending on the embodiment) 70), some or all of the analysis unit 80 and the control unit 90 may be integrated in the same one device (eg, a display device). In addition, each of the parts (10, 30, 40, 50, 60, 70, 80, 90) is only functionally classifying devices according to the operation performed by the computing device in which they are implemented, and must be separated from each other. Does not mean

The auction unit 10 auctions a product to be sold by the seller. In one embodiment, the auction unit 10 includes an auction generating unit 110 and a bid proceeding unit 130. The auction unit 10 receives auction information for a product, and provides the auction information to a user. Here, the auction information includes seller information, product quantity information, starting price information, maximum estimated price information, and the like.

In the present specification, the product is an auction target, and includes tangible products as well as intangible products. Here, the tangible product is a product of a relatively limited quantity, and includes art works, crafts (for example, accessories, tea cups, bowls, glass products, etc.), and intangible products are information for producing tangible products (for example, , Expected sketches for tangible products, materials to be used in the production of tangible products, etc.). Here, the artwork is an original work, a replica reproduced in a limited quantity based on the original work, and includes a large number of products without an edition number and/or limited edition products with an edition number, and edited editions based on the original work. do.

In the present specification, the original work is a product that is not a reproduction, and represents a product produced by reflecting the artist's intention for the first time. The original work is the basis for various secondary productions, for example, replicas (digital prints, etc.) that reproduce the original work, edits based on the original work, or edits adapted to other media. The original work may be more than one. The creator of the original work may be the author himself who planned the original work, or another person who received the original work plan from the author. Hereinafter, for clarity of description, the product may refer to a tangible product such as a digital print, but is not limited thereto.

The seller information is information related to the seller who sells the product, and includes the name, biography, and representative work of the product seller.

The product quantity represents the quantity of products that can be sold through an auction (that is, the auction quantity). For example, for an auction that sells all replicas with an edition number, the last edition number corresponds to the total product quantity.

The starting price is the auction starting price and is the minimum amount that can be bid at the auction, indicating the minimum guaranteed amount that the seller wants to sell for his or her product. The starting price is determined based on the original production cost of materials, time, etc., used to produce the seller's product, and the additional production cost, etc., used to produce the duplicated product (eg, digital print) generated based on the original. In addition, in another embodiment, it may be determined based on auction history for other products in the past.

The maximum estimated price represents the maximum amount that can be bid at the auction. In one embodiment, the maximum estimate is determined based on the evaluation of a distribution service provider or expert. In another embodiment, the maximum estimate is determined based on auction history for other products in the past.

In some embodiments, the auction information is information that may influence a user's auction participation and bid decision, and may further include product characteristics including, for example, a product type, size, and expert evaluation.

In one embodiment, the auction generating unit 110 may provide auction information through a user terminal connected to the product distribution server 1. Here, the user includes a service member of a distribution service provider operating the product distribution server 1 as well as a non-service member. Various user terminals may be used to receive auction information. For example, the user terminal may include a personal computer (PC), a laptop, a tablet, a mobile phone, a smart phone, and a portable information terminal (PDA).

In some embodiments, the auction creation unit 110 may provide different auction information for each user. For example, the auction generating unit 110 may provide information useful for conducting an auction, such as a past auction history for other products, only to member users, but may not provide the information to non-member users.

The bidding progress unit 130 receives bidding information including a bidder from one or more bidders. The bidding information is generated by the user terminal.

The bidding information includes the bidder's identification information including the bidder's nickname, name, address, and the like, and the bidder suggested for purchasing the product. Here, the bid price is the amount between the maximum estimated price and the starting price. In this way, the bidder always corresponds to the amount of the starting price or more, so the seller can be guaranteed the minimum amount spent for the production of the product.

In some embodiments, the bidding information may further include bidding quantity information that the bidder wishes to purchase. Here, the bid quantity is an integer between 1 and n, where n represents the quantity of the product.

In one embodiment, the bid proceeding unit 130 receives the bid information until a preset time elapses. When the preset time has elapsed, the bidding information is no longer received.

In one embodiment, when the bidding proceeding unit 130 presents a bid price (a bid price exceeding the maximum estimated price, or a bid price below the starting price) outside the bidding amount range, the bidder rejects the bidding and responds to the bid price Send a notification. In another embodiment, the bid proceeding unit 130 may prevent a bidder from entering a bidding amount out of a bidding amount range when the bidder inputs the bidding information to the user terminal.

The successful bidder determination unit 40 determines a successful bidder group including the lowest successful bidder based on the product quantity information and the bidding information when a predetermined time has elapsed. The number of successful bidders included in the successful bidder group may be one to the number corresponding to the number of products of the auction. The lowest successful bidder represents the successful bidder who has given the lowest bid price among the successful bidders included in the successful bidder group.

In one embodiment, the successful bidder determination unit 40 may determine the successful bidder group further based on the bidding time of each successful bidder when the number of bidders in the successful bidder group exceeds the number of merchandise. That is, the successful bidder determination unit 40 may determine the lowest successful bidders on a first-come, first-served basis.

Additionally, the successful bidder determination unit 40 may determine the number of products to be provided for each successful bidder. In one embodiment, the successful bidder determination unit 40 is the lowest successful bidder of some of the bidding quantity suggested by the lowest successful bidder so that if the bidding quantity to be purchased by the successful bidders included in the successful bidder group exceeds the quantity of goods Decide on the quantity to be provided.

In addition, the successful bidder determination unit 40 may determine that some of the successful bidders are additional successful bidders according to the bid price of the successful bidders when some successful bidders do not settle.

The selling price determination unit 50 determines the selling price as the selling price between the bidding price in the successful bidder group and the starting price, which is the minimum guaranteed amount desired by the seller.

In one embodiment, the selling price determination unit 50 determines the selling price for the product as the highest bidding price among the bidding prices of non-winning bidders who are not included in the winning bidder group.

In one embodiment, the selling price determination unit 50 determines a sales price for collective application to all successful bidders included in the successful bidder group. In another embodiment, the selling price determination unit 50 differentially determines the selling price for the product.

In one embodiment, the selling price determining unit 50 additionally determines the selling price for the additional successful bidder. In this case, the sales price applied to the existing successful bidders and additional successful bidders is determined respectively.

The payment unit 60 applies the sale price determined for the successful bidder group to the successful bidder group to request payment from the successful bidder included in the successful bidder group. For example, the payment unit 60 transmits a payment request through the user terminal of the successful bidder to the determined selling price for the successful bidder group. The payment unit 60 records payment time and the like in response to the successful bidder's payment.

In addition, the payment unit 60 calculates a fee for the seller based on the amount of the selling price minus the starting price. In this way, the seller pays the commission outside the starting price range, so the seller can always be guaranteed the desired amount. Here, the fee includes an entry fee, and a fee that may be generated in other distribution processes, such as a distribution fee, and may be referred to as a specific fee (eg, purchase fee) depending on the object to be charged.

In addition, the successful bidder receives the product when paying the amount corresponding to the determined selling price. Therefore, the purchase price is not included in the payment amount of the successful bidder, and the successful bidder can know more precisely about the basis on which the purchase fee is calculated. As a result, the fixed purchase fee arbitrarily set by the intermediary is settled, and the successful bidder solves the unreasonable problem of the existing auction of existing art that includes the unreasonable purchase fee in the successful bidder's payment amount, and provides economic benefits to the buyer. do. Furthermore, the distribution service provider can automatically settle the fee based on the amount of the selling price minus the starting price, thereby minimizing resource consumption (e.g., time to set the fee) that may occur between the seller and the distribution service provider. Can.

The control unit 90 controls the overall operation performed by the product distribution server 1. For example, the control unit 90 is a component of the product distribution server 1, the auction creation unit 110, the bid proceeding unit 130, the successful bidder determination unit 40, the selling price determination unit 50, and the payment unit The operation of (60) can be controlled.

In addition, the control unit 90 may provide some and/or all of the auction related information to the seller of the auction. Here, the information related to the auction includes how many people participated in the bid, the maximum successful bid price, the minimum successful bid price, and the basis for calculating the fee.

In addition, the control unit 90 may provide certain benefits to some or all of the subgroups of the successful bidder group, or to each successful bidder of the successful bidder group. The schedule benefits may include points earning benefits, discount benefits, edition numbering, and auxiliary characteristics related to product sales (eg, frame color of a digital print, material, etc.). In some embodiments, certain benefits may be differentially provided. In addition, the selling price applied to the successful bidder may additionally be differentially applied. For example, when a picture frame option is set and a picture frame of a different price is provided with a product, the picture frame price may be differentially applied. Furthermore, the benefit can be announced to the user.

In one embodiment, the control unit 90 may provide a certain benefit to some or all of the successful bidder group according to the bidder. In one example, the control unit 90 may assign the edition numbers 1 to 10 to the first subgroup, and the edition numbers 11 to 20 to the second subgroup. In another example, the controller 90 may provide the first sub-group with a frame having a first color that is popular even though it is the same product, and the second sub-group may provide a frame with a second color that is less popular. have. In another example, the control unit 90 points differently by% of the bidder ranking, such as 10% of the determined sales price for the 1st bidder, 9% for the 2nd bidder, and 1% for the 10th bidder. To earn. In another example, the control unit 90 may be used for an additional discount of 10% of the determined sales price for the first bidder ranking, 9% of the determined sales price for the second ranking, ..., and 1% for the 10th ranking. You can make payment.

In addition, the control unit 90 is not determined as a successful bidder, but may also provide a certain benefit to non-winners who helped determine the sales price.

In one embodiment, when the auction product is the original, the control unit 90 may transmit a request for confirming whether or not to purchase a duplicate based on the original to the non-winner. The control unit 90 may use this after receiving an answer as to whether to purchase. For example, after the auction of the original work, the result of the answer may be provided to a seller who has yet to sell digital prints. In addition, when an auction of a digital print is scheduled after an auction of the original work, auction information for the digital print may be provided to a non-winner who has said that he or she will purchase.

Additionally, the control unit 90 may automatically perform payment processing of the sales price at the end of the auction to the user who participated in the auction. For this reason, it is possible to prevent false bidding, successful bidder departure, and the like.

In one embodiment, the product distribution server 1 may further include a storage unit 70 and an analysis unit 80. The storage unit 70 is a database that stores auction history information for an auction in progress, where the database means a collection of large amounts of structured, unstructured, or semi-structured data, and can store data related to auctions that have already been performed in the past. . Here, the structured data is data stored in a fixed field, for example, a relational database, a spreadsheet, or the like. In addition, the unstructured data is data that is not stored in a fixed field, for example, text documents, images, videos, audio data, and the like. Further, the semi-structured data is not stored in a fixed field, but is data including metadata or a schema, for example, XML, HTML, text, and the like.

The auction history information may include various information about the auction that has been performed, for example, the seller's identity, the characteristics of the product subject to the auction, the quantity of the product sold, the lowest, maximum, average bid price, whether to bid, etc. in the bid price suggested by the bidder. In addition, the auction history information may further include information related to a bidder, such as a bidder and a successful bidder, who has bid at what price. Information related to these auction participants can be used to secure potential customers. For example, based on the information related to the auction participants, the server 1 may provide auction information, notifications, and the like for other products similar to the products of the auction. Alternatively, when the participating auction product is an original work, auction information, notifications, and the like for a duplicate work such as a digital print may be provided.

In addition, the storage unit 70 includes various information about the user who participated in the auction conducted by the product distribution server 1, the user's identity, the number of attempts by the user, the number of successful bids, the number of final payments, the auction in which the user participated (for example , User information related to auction type, author, and the like).

The analysis unit 80 is configured to analyze the data stored in the storage unit 70 to derive the result when the auction ends and payment for the auction is completed (or when a predetermined time has elapsed).

3 is a conceptual diagram illustrating a structure in which auction history information is analyzed according to an embodiment of the present invention.

In one embodiment, the analysis unit 80 is a batch system that generates batch data by performing map-reduce processing of raw data at a preset time for large-scale big data analysis. (batch system). For example, the analysis unit 80 is composed of a Lambda Architecture that mixes pre-made batch data and real-time data through batch processing.

Referring to FIG. 3, in the lambda architecture, a batch layer that generates batch views (batch tables) by batch processing the stored raw data individually or at regular intervals is stored, and batch-processed data is stored and processed. It can be modified only in a normal case, and normally only reads data, and includes a serving layer to prevent batch data from being changed or contaminated, and a speed layer to aggregate a real-time view (real-time table). The batch layer performs batch processing through map-reduce, and the speed layer rapidly processes real-time streaming data. Next, a query is generated by combining a parallel-processed real-time view and a batch view. In this way, the product distribution server 1 can perform rapid data analysis through parallel data processing.

Through the above-described structure and process, the analysis unit 80 may derive an analysis result that is used to determine an appropriate quantity and/or an appropriate maximum estimated value of a product in an auction. The result of this analysis can also be used to determine the starting price of an auction for another product, such as another product of the same seller, the same and/or similar products of another seller.

The results analyzed by the analysis unit 80 may be added to the auction history information used to derive the corresponding results, or may be stored in the storage unit 70 again as separate new information. Furthermore, the results analyzed by the analysis unit 80 may be provided to a distribution service provider.

Additionally, the product distribution server 1 may further include a prediction unit 100 that calculates a value of an appropriate maximum estimated price and/or an appropriate product quantity for an auction for a specific product having a specific starting price.

In one embodiment, the prediction unit 100, in the past auction for other products having a starting price specified by the seller or a similar amount within the same or a predetermined range as the starting price, the auction history, such as the bid price of the past bidder, the successful bidder The maximum estimate can be determined based on the information. In the above embodiment, the prediction unit 100 may further determine the maximum estimated price based on product characteristics such as product type and size.

In one embodiment, the prediction unit 100 may generate a patrol probability model that calculates a patrol probability according to a start price and a product quantity based on a start price and a product quantity among auction history information that has been toured in the past. Using the patrol likelihood model, it is possible to determine not only the probability of patrol but also the quantity of products having a predetermined probability or more in the case of a specific product quantity. In some embodiments, the likelihood model may be generated based further on seller identity, product characteristics, and the like.

The likelihood model may be applied to the quantity of products included in the received auction information before receiving the auction information to the user after receiving the auction information. Can be calculated.

The likelihood model can be generated through support vector machine (SVM), support vector data description (SVDD), support vector regression (SVR), artificial neural network (ANN), and K-means clustering algorithm. However, the present invention is not limited thereto, and may be generated by various machine learning algorithms.

In another embodiment, the product distribution server 1 may store and call auction history information in an external database for use.

It will be apparent to those skilled in the art that the product distribution server 1 may include other components. It may also include other hardware elements needed for the operations described herein, including, for example, an input device for data entry and an output device for printing or other data display. In addition, it may further include a network, a network interface and a protocol for connecting the product distribution server 1 and an external device (for example, a user terminal).

4 is a flowchart of a product distribution method using an auction according to an embodiment of the present invention.

Referring to FIG. 4, in one embodiment, the product distribution server 1 receives auction information for a product that a seller wants to sell (S10). The auction information includes the seller's identity, the quantity of the product, the starting price and the maximum estimated price suggested by the seller. In addition, auction information may include a policy for auction. For example, the auction information may include policies such as the quantity of products that can be sold to one successful bidder. In addition, the auction information includes the time when the auction ends.

In one embodiment, the product distribution server 1 may receive the maximum estimated price from a distribution service provider operating the product distribution server 1. In another embodiment, the product distribution server 1 may receive the maximum estimated value determined based on the auction history information of another product from inside and/or outside the product distribution server 1.

The product distribution server 1 provides auction information to the user in order to induce the user to participate in the auction (S20). The user interested in the corresponding product determines whether to bid based on the maximum estimated price, the starting price, and other auction information (seller information, product characteristic information, etc.) included in the auction information, and inputs the bidding information to the user terminal.

The product distribution server 1 receives the bidding information including the bidding price between the maximum estimated price and the starting price from one or more bidders (S30). In some embodiments, the bidding information may further include a bidding quantity that is a quantity the bidder wants to purchase. In one embodiment, the bidding information is received up to a preset time, that is, an auction end time.

When the preset time has elapsed, the product distribution server 1 determines a successful bidder group including the lowest successful bidder (S40). The winning group is determined in a variety of ways. In one embodiment, the product distribution server 1 determines the successful bidder group based on the product quantity information and the bidding information. For example, if the number of products is 30 (that is, when the edition number is 1 to 30), the bidders are descended according to the bid price, and then the group of successful bidders is determined according to the product quantity. The successful bidder group may consist of a minimum of 1 to a maximum of 30.

Thereafter, the product distribution server 1 may determine the selling price for the product based on the bid price of the lowest successful bidder and/or the bid price of the non-winner who is not included in the successful bidder group (S50). In one embodiment, the product distribution server 1 determines the selling price for the product as the highest bid among the bidders of the non-winner who are smaller than the bid of the lowest successful bidder.

5 is a diagram exemplarily showing a process of determining a successful bidder group and a selling price in an auction according to an embodiment of the present invention. In the auction of FIG. 5, the number of digital prints is 30, the maximum estimated price is 1 million won, and the starting price is 500,000 won, the first bidder (X1) to the 28th bidder (X28), the 30th bidder (X30) and the 31st The bidder X31 intends to purchase one product per person, and the 29th bidder X29 is to illustrate the case of purchasing two products.

Referring to FIG. 5, the product distribution server 1 determines a total of 29 successful bidders from the first bidder X1 to the 29th bidder X29 based on the number of digital prints (30) and the bidder. . In this case, the bid price of the lowest successful bidder (29th bidder (X29)) is \670,000. Looking at the bid price of non-winners who are not included in the successful bidder group, the bid price of the 30th bidder (X30) who is the non-winner is \670,000, which is the same as the bid price of the lowest successful bidder, and the bid price of the other 31st bidder (X31) is \640,000 The bidding price of the 32nd bidder (X32), another non-winner, is \620,000. The product distribution server 1 determines the highest bid price (\640,000) among the bid price bidders who are smaller than the lowest bid bid price (\670,000) as the selling price.

Referring to FIG. 5 again, payment is requested by collectively applying the sale price determined in step S50 to the successful bidder group (S60). When payment of the successful bidder is performed in response to the payment request in step S60, the product distribution server 1 may settle a fee based on the amount of the selling price minus the starting price (S65). Referring to FIG. 5 again, a fee is settled based on the amount (\170,000) obtained by subtracting the starting price (\500,000) from the selling price (\670,000) (S65).

Additionally, the product distribution server 1 may automatically perform payment processing of the sales price at the end of the auction to a user who participated in the auction. In one example, a user may register a payment method (eg, a credit card) prior to payment of a sales price (eg, when participating in an auction), and be automatically settled when the final successful bid is made. For this reason, it is possible to prevent false bidding, successful bidder departure, and the like.

Additionally, the product distribution server 1 may create one or more subgroups in the successful bidder group and then differentially determine the selling price applied to each subgroup.

In one embodiment, the product distribution server 1 creates one or more subgroups according to the bid price in the successful bidder group. In one example, one or more sub-groups may be generated based on a bid ranking according to bid prices.

Referring to FIG. 5 again, a first subgroup including the first responders X1 to the tenth responder X10, a second subgroup including the eleventh responders X11 to the twentieth responder X20, and the twenty-first A third sub-group including the responders X21 to 29th responders X29 may be generated. In another example, after calculating the difference between the bidders of the bidders having adjacent bid rankings, one or more sub-groups may be generated based on the bidder gaps.

Subsequently, the highest bid price among the lowest bid price of the lowest successful bidder in each sub-group is determined as the selling price for each sub-group. In the above example, among the bidders lower than the bidder (\950,000) of the lowest successful bidder (the 10th bidder (X10)) in the first subgroup, the highest bidder is the bidder (\850,000) of the 11th bidder (X11), so the first The selling price applied to all successful bidders included in the sub-group (first bidders X1 to 10th bidders X10) is determined to be \850,000. In addition, the highest bid among the lowest bidders (\810,000) of the lowest successful bidder (20th bidder (X20)) in the second subgroup is included in the second subgroup because the 21st bidder (X21) is the bidder (\800,000). The selling price applied to all successful bidders (11th bidder (X11) to 20th bidder (X20)) is determined to be \800,000. In addition, the highest bidder among the lower bidders (\670,000) in the third subgroup (the 21st bidder (X21) to the 29th bidder (X29)) (the 29th bidder (X29)) is the 30th bidder ( X30) is not the bidder (\670,000) but the 31st bidder (X31). Therefore, the selling price applied to the third sub-group is determined to be \640,000. In this way, different selling prices (\850,000, \800,000, \640,000) between the successful bidders included in the same successful bidder group may be determined, respectively.

Subsequently, the determined selling price is collectively applied to each sub-group to request payment (S60), and when the successful bidder's payment is performed in response to the payment request in step S60, the product distribution server 1 determines the selling price of each sub-group. A fee for each sub-group may be settled based on the amount obtained by subtracting the starting price (S65).

Additionally, if some of the successful bidder groups do not settle the selling price, the successful bidder group may be updated based on the successful bid price of the non- successful bidder. In this case, the bidder who has been renewed as the new lowest successful bidder determines the bid price as the selling price and requests payment.

The added successful bidder has the advantage of purchasing a product at a desired amount at the time of bidding for a product for which a purchase opportunity is not given, and the seller and the distribution service provider have an advantage of compensating for the loss that may have occurred due to outstanding payment.

Subsequently, the fee for the added bidder is settled. In one embodiment, a fee for an additional successful bidder may be settled based on the amount of the starting price minus the selling price for the added successful bidder.

Referring to FIG. 5 again, for example, when the 29th bidder X29 has not paid for the sales price, the 30th bidder X30 and the 31st bidder X31 are determined as additional successful bidders. The fee for the 30th bidder (X30), the additional successful bidder, is settled based on the amount (\170,000) minus the starting price (\500,000) from the selling price (\670,000) for the additional successful bidder, and the 31st bidder, the other additional successful bidder (31) The fee for X31) is settled based on the price (\14,000) minus the starting price (\500,000) from the selling price (\640,000) for additional successful bidders.

In some embodiments, if the bid price of the additional successful bidder is lower than the previously determined selling price, the selling price applied to the additional successful bidder may be determined as the previously determined selling price. Due to this, the problem of equity with other successful bidders and the possibility of abuse through deliberate inspection can be reduced.

In some other embodiments, for the benefit of promotion, the selling price applied to the additional successful bidder may be determined as the successful bidding price of the additional successful bidder even if the bidding price of the additional successful bidder is lower than the previously determined selling price.

Additionally, there may be a case in which the auction has ended, but the non-winner does not exist (for example, when the sum of the bidders' bid quantity is equal to the product quantity). In this case, the selling price may be determined by a process different from the process described above. In one embodiment, the selling price may be determined as the bid price of the lowest successful bidder included in the winning bidder group.

In another embodiment, the predicted bid price may be determined as a selling price after predicting the bid price of the additional bidder based on the bid price of the successful bidder group. For example, by applying the bid price from the successful bidder group to the linear regression analysis algorithm, predict the bid price of additional bidders (that is, the bidders next to the lowest successful bidder) who can bid at a lower price than the lowest successful bidder, and then predicted additional bidders The bid price can be determined as the selling price.

In another embodiment, the auction may be terminated and the transaction may be concluded even if the total amount of the bidder's bid amount is small until a preset time. In this case, a process of determining the selling price may be applied when the above-mentioned non-winner does not exist. In another embodiment, if the sum of the bidders' bidding quantity is small by a predetermined time, the auction itself may be inspected and the sale may not be successful.

Additionally, after receiving the auction information and before providing the auction information to the user, it is possible to calculate the likelihood of bidding during the auction with the quantity of products included in the received auction information. In one embodiment, the likelihood of bidding may be calculated by applying the commodity quantity and starting price included in the received auction information to the bidding likelihood model. Here, the patrol likelihood model is based on the product quantity and the starting price included in the received auction information, support vector machine (SVM), support vector data description (SVDD), support vector regression (SVR), artificial neural network (ANN), K It can be generated using a machine learning algorithm, such as an average clustering (K-means clustering) algorithm.

In one embodiment, when the likelihood of a bid is less than a predetermined likelihood, a bid alert may be sent to the seller. Due to this, the product distribution server 1 can increase the probability that the auction is successfully closed without being visited.

Additionally, if the likelihood of the bidding is less than a predetermined likelihood, the minimum quantity of the product that is not to be inspected (ie, the quantity of goods corresponding to the predetermined likelihood) may be recommended to the seller along with a warning of the bidding. Furthermore, the product quantity may be changed to a product quantity corresponding to the predetermined possibility, or may be changed to a different product based on the product quantity corresponding to the predetermined possibility.

Additionally, when the product is an original work, a process of transmitting a request for confirming whether to purchase a replica of the original work to the non-winner of the auction may be further performed. Based on the result of the non-winner's purchase demand for replicas, it may be determined whether the replicas are to be sold, how many copies are to be sold, and the like. Through this, in the case of a new artist with little or no recognition, the distribution time exposed to consumers in the work market can be longer, and sales plans can be made more efficiently when selling replicas.

Operations by the product distribution server and method using auction according to the above-described embodiments may be implemented at least partially as a computer program, and recorded in a computer-readable recording medium. For example, implemented with a program product consisting of a computer-readable medium containing program code, which may be executed by a processor to perform any or all of the steps, operations, or procedures described.

The computer-readable recording medium includes all types of recording devices in which data that can be read by a computer are stored. Examples of computer-readable recording media include ROM, RAM, CD-ROM, magnetic tape, floppy disks, and optical data storage devices. In addition, the computer readable recording medium may be distributed over network coupled computer systems so that the computer readable code is stored and executed in a distributed manner. In addition, functional programs, codes, and code segments for implementing the present embodiment will be readily understood by those skilled in the art to which this embodiment belongs.

The present invention described above has been described with reference to the embodiments shown in the drawings, but this is merely exemplary, and those skilled in the art will understand that various modifications and modifications of the embodiments are possible therefrom. However, it should be considered that such modifications are within the technical protection scope of the present invention. Therefore, the true technical protection scope of the present invention should be defined by the technical spirit of the appended claims.

According to a recent survey of the art market, the number of transactions in works has been showing a modest increase, while the number of works has been increasing rapidly. In addition, the activities of young artists are also becoming active.

One of the difficulties in this young artist's work is the calculation of sales fees such as the entry fee. Through the present invention, the work can be sold at a price higher than the minimum guaranteed amount desired by the new artist, and the commission can be settled immediately based on the difference, minimizing the time that can be spent negotiating the fee between the broker and the seller. It can be useful to do. In addition, the ridiculous works due to high fees can be reduced, which is advantageous for both sellers and buyers.

In particular, a machine learning algorithm, which is one of the fourth industrial technologies, can be used to objectively estimate a necessary number (for example, the maximum estimated price) associated with a product auction, thereby reducing distrust due to information imbalance between the seller and the broker.

In addition, since the subject of sale includes not only the original work, but also a copy of the artist's handwritten signature, limited edition edition number, etc., and a replica that is sold ('digital print', etc.), it can contribute to the popularization of art works. Furthermore, the seller can obtain the effect of increasing income and recognition other than the original, and the buyer can enjoy the opportunity for appreciation of the work and the artistic aspect of collecting limited editions, and the high price is not necessarily successful, but without knowing how much You can purchase the product at a price lower than the price you suggested, so you can get the pleasure of gaming performance, such as establishing a product purchasing strategy.

Claims (12)

A product distribution method using an auction performed by a product distribution server,
Receiving auction information for a product provided by a seller-the auction information includes seller information, a product quantity, a maximum estimated price for the product, and a starting price suggested by the seller, wherein the maximum estimated price is an amount higher than the starting price being;
Providing the auction information to a user;
Receiving bidding information including a bidding price between the maximum estimated price and the starting price and a bidding quantity less than or equal to the product quantity from one or more bidders;
Determining a successful bidder group based on the product quantity and the bidding information when a preset time has elapsed;
Determining a highest bid price of a bid price of a non-bid successful bidder with a lower bid price of the lowest bid price of the successful bidder group as the selling price for the product;
Requesting payment by applying the sale price to the successful bidder group;
Calculating the likelihood of patrol when auctioning with the product quantity based on the product quantity and the starting price included in the received auction information, before receiving the auction information to the user after receiving the seller's auction information;
When the probability of the bidding is less than a predetermined probability, sending a bidding warning to the seller;
Transmitting a quantity of products corresponding to the predetermined probability to the seller as a product quantity recommendation when the probability of the observation is less than a predetermined probability based on a result of calculating the probability of the bidding; And
The step of providing a certain benefit to each individual of the successful bidders group,
The step of determining the highest bid price among the bid bidders of the non-win bidders who bid at the lower bid price of the lowest bidder of the successful bidder group is the selling price for the product:
If the auction is over but there is no non-winner, predicting the bid price of the non-winner based on the bid price of one or more successful bidders in the successful bidder group, and determining the predicted bid price as the selling price for the product,
The maximum estimated price is determined based on auction history information of a past auction for another product having a starting price equal to or equal to a starting price specified by a seller as a starting price. According to claim 1,
The step of determining the selling price,
Determining one or more subgroups from the bidders in the winning bidder group;
And determining the highest bid price among the bidders of the bidders lower than the bid price of the lowest successful bidders in each subgroup as the selling price for each subgroup. delete According to claim 1,
The product distribution server further comprises the step of calculating a fee for the seller based on the amount of the selling price minus the starting price. According to claim 1,
The product distribution server further comprises the step of differentially granting a certain benefit based on the bid price of the successful bidder included in the successful bidder group. According to claim 1,
And when some of the successful bidders included in the successful bidder group do not perform payment in response to a payment request, updating the successful bidder group to add successful bidders based on the bid price of the non- successful bidder. The method of claim 6,
The additional successful bidder further comprises the step of determining the bid price of the additional successful bidders as a sale price. delete delete According to claim 1,
The likelihood is generated using at least one of a support vector machine (SVM), support vector data description (SVDD), support vector regression (SVR), artificial neural network (ANN), and K-means clustering algorithm. Product distribution method, characterized in that it is calculated by the observation possibility model. According to claim 1,
And if the product is the original, sending a request to the non-winner of the auction to confirm whether to purchase a duplicate of the product. As a product distribution server using auction,
An auction unit that receives auction information for a product, provides the auction information to a user, and receives bid information including a bid price and a bid quantity from one or more bidders-the auction information includes seller information, product quantity information, and the product Includes a maximum estimated price for the seller and a starting price suggested by the seller, wherein the bid price is an amount between the maximum estimated price and the starting price, the bid quantity is less than the commodity quantity, and the maximum estimated price is an amount higher than the starting price;
A successful bidder determination unit determining a successful bidder group based on the product quantity information and the bidding information when a preset time has elapsed;
A selling price determination unit that determines a highest bid price among non-win bid bidders who bid at a lower bid price than the lowest bid price of the successful bidder group; And
A payment unit for requesting payment by applying the selling price to the successful bidder group; And
The maximum estimated price is determined based on auction history information of a past auction for another product having a starting price equal to or equal to the starting price specified by the seller, and based on the product quantity and the starting price included in the received auction information. It includes a prediction unit for calculating the likelihood of the bidding during the auction proceeds with the product quantity,
The auction department:
If the probability of the bidding is less than a predetermined probability, a warning alert is transmitted to the seller, and when the probability of the bidding is less than a predetermined probability based on the result of calculating the bidding probability, the product quantity corresponding to the predetermined probability is referred to as a product quantity recommendation. It is further configured to transmit to the seller, and provide certain benefits to the individual of the successful bidder group,
The selling price determination unit,
When the auction is over but there are no non-winners, it is further configured to predict the bid price of the non-winner based on the bid price of one or more successful bidders in the group of successful bidders, and determine the predicted bid price as the selling price for the product. Product distribution server.

Images